Bas Veeling
Senior Research Engineer at Microsoft
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolBas Veeling is a Senior Research Engineer based in Amsterdam with 15 years of experience applying machine learning to scientific and medical domains. Currently at Microsoft on AI4Science, he combines computational chemistry and molecular simulation expertise with deep learning to drive research-grade solutions. His academic background includes a cum laude Master's and a PhD in machine learning for health under Max Welling, plus research stints at Google Brain exploring model uncertainty. Bas has a strong track record of translating research into practical tools—evidenced by his Keras WaveNet contributions and a history of consulting and product-focused engineering for startups and enterprises. He blends hands-on model development with production awareness, from prototyping novel architectures to integrating datasets like VCTK for speech tasks. Colleagues describe him as the kind of researcher-engineer who surfaces unexpected practical improvements from theoretical work.
15 years of coding experience
14 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Universiteit TwenteMaster’s Degree, Artificial Intelligence, Cum laude, Master’s Degree, Artificial Intelligence, Cum laude at University of AmsterdamHigh School, VWO Nature and Technology, High School, VWO Nature and Technology at Montessori Lyceum Herman Jordan
Dutch, English, German
Stackoverflow
834reputation
84kreached
5answers
11questions
Github Skills (13)
keras10
machine-learning10
deep-learning10
python10
tensorflow9
emberjs6
capistrano6
database-relations6
php6
mongodb6
database6
mysql6
antlr6
Programming languages (10)
Github contributions (5)
basveeling/wavenet
Sep 2016 - Jul 2018
Keras WaveNet implementation
Role in this project:
ML Engineer
ML EngineerContributions:55 commits, 3 PRs, 48 pushes in 1 year 10 months
Contributions summary:Bas primarily focused on the implementation and modification of a WaveNet architecture using Keras. Their contributions include adding features for initial input during prediction, fixing naming compatibility issues, and introducing options to use the VCTK dataset. Furthermore, the user made significant changes to prediction configurations, and introduced soft targets for training, demonstrating a focus on model development and enhancement.
deep-learningkeras-neural-networkstheanokeras-tensorflowwavenet
basveeling/vertalerbouw
May 2014 - Jul 2014
Contributions:61 commits, 1 comment in 1 month
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